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Cognitive modulation of local and callosal neural interactions in decision making.

Merchant H, Crowe DA, Fortes AF, Georgopoulos AP - Front Neurosci (2014)

Bottom Line: They were observed both within area 7a of the posterior parietal cortex and between symmetric 7a areas of the two hemispheres.Time lags for maximum interactions were longer for opposite- vs. same-hemisphere recordings, and lags for negative interactions were longer than for positive interactions in both recording sites.These findings underscore the involvement of dynamic neuronal interactions in cognitive processing within and across hemispheres.

View Article: PubMed Central - PubMed

Affiliation: Department of Behavioral and Cognitive Neurobiology, Instituto de Neurobiología, UNAM Querétaro, México.

ABSTRACT
Traditionally, the neurophysiological mechanisms of cognitive processing have been investigated at the single cell level. Here we show that the dynamic, millisecond-by-millisecond, interactions between neuronal events measured by local field potentials are modulated in an orderly fashion by key task variables of a space categorization task performed by monkeys. These interactions were stronger during periods of higher cognitive load and varied in sign (positive, negative). They were observed both within area 7a of the posterior parietal cortex and between symmetric 7a areas of the two hemispheres. Time lags for maximum interactions were longer for opposite- vs. same-hemisphere recordings, and lags for negative interactions were longer than for positive interactions in both recording sites. These findings underscore the involvement of dynamic neuronal interactions in cognitive processing within and across hemispheres. They also provide accurate estimates of lags in callosal interactions, very comparable to similar estimates of callosal conduction delays derived from neuroanatomical measurements (Caminiti et al., 2013).

No MeSH data available.


Related in: MedlinePlus

Examples from two LPF recordings and their preprocessing for prewhitening. Plots are shown for raw data, data after first-order differencing, and after applying an ARIMA [25,1,1] model. In addition, plots of autocorrelation function (ACF) and partial autocorrelation function (PACF) are shown for the different stages of data processing. It can be seen that the prewhitened data (right-most column) are devoid of any internal dependencies and evidenced by the flat ACF and PACF plots.
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Figure 3: Examples from two LPF recordings and their preprocessing for prewhitening. Plots are shown for raw data, data after first-order differencing, and after applying an ARIMA [25,1,1] model. In addition, plots of autocorrelation function (ACF) and partial autocorrelation function (PACF) are shown for the different stages of data processing. It can be seen that the prewhitened data (right-most column) are devoid of any internal dependencies and evidenced by the flat ACF and PACF plots.

Mentions: The raw digitized extracellular record (collected at 40 kHz) was resampled (decimated) to 1 kHz, by picking every 40th sample of the original digitized data. The resulting time series were then prewhitened by taking the residuals after applying an [25,1,1] AutoRegressive Integrative Moving Average (ARIMA) model (Box and Jenkins, 1970; Priestley, 1981) (see Figure 3 for details). This model was arrived at after extensive model identification and yielded residuals (innovations) that were practically stationary with respect to the mean and variance, and flat autocorrelations (see Results below). The crosscorrelation function (±25 ms maximum lag) (Figure 4) was computed for all pairs of recorded LFP time series and for each one of the seven periods of the task (Figure 1). The following three measures were extracted from each crosscorrelogram: (a) The crosscorrelation at zero lag (CC0), indicating synchronicity, (b) the crosscorrelation with the maximum absolute value (CCmax), and (c) the lag at which CCmax occurred. For the purposes of this analysis, the sign of the lag was ignored, hence its absolute value was used (range: 0–25 ms); hereafter, we use “lag” to mean “absolute value of lag.” The signs of CC0 and CCmax were retained and their statistical significance calculated using the assumption that the series are white noise. The crosscorrelations were z-transformed to normalize their distribution (Fisher, 1958):


Cognitive modulation of local and callosal neural interactions in decision making.

Merchant H, Crowe DA, Fortes AF, Georgopoulos AP - Front Neurosci (2014)

Examples from two LPF recordings and their preprocessing for prewhitening. Plots are shown for raw data, data after first-order differencing, and after applying an ARIMA [25,1,1] model. In addition, plots of autocorrelation function (ACF) and partial autocorrelation function (PACF) are shown for the different stages of data processing. It can be seen that the prewhitened data (right-most column) are devoid of any internal dependencies and evidenced by the flat ACF and PACF plots.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4128092&req=5

Figure 3: Examples from two LPF recordings and their preprocessing for prewhitening. Plots are shown for raw data, data after first-order differencing, and after applying an ARIMA [25,1,1] model. In addition, plots of autocorrelation function (ACF) and partial autocorrelation function (PACF) are shown for the different stages of data processing. It can be seen that the prewhitened data (right-most column) are devoid of any internal dependencies and evidenced by the flat ACF and PACF plots.
Mentions: The raw digitized extracellular record (collected at 40 kHz) was resampled (decimated) to 1 kHz, by picking every 40th sample of the original digitized data. The resulting time series were then prewhitened by taking the residuals after applying an [25,1,1] AutoRegressive Integrative Moving Average (ARIMA) model (Box and Jenkins, 1970; Priestley, 1981) (see Figure 3 for details). This model was arrived at after extensive model identification and yielded residuals (innovations) that were practically stationary with respect to the mean and variance, and flat autocorrelations (see Results below). The crosscorrelation function (±25 ms maximum lag) (Figure 4) was computed for all pairs of recorded LFP time series and for each one of the seven periods of the task (Figure 1). The following three measures were extracted from each crosscorrelogram: (a) The crosscorrelation at zero lag (CC0), indicating synchronicity, (b) the crosscorrelation with the maximum absolute value (CCmax), and (c) the lag at which CCmax occurred. For the purposes of this analysis, the sign of the lag was ignored, hence its absolute value was used (range: 0–25 ms); hereafter, we use “lag” to mean “absolute value of lag.” The signs of CC0 and CCmax were retained and their statistical significance calculated using the assumption that the series are white noise. The crosscorrelations were z-transformed to normalize their distribution (Fisher, 1958):

Bottom Line: They were observed both within area 7a of the posterior parietal cortex and between symmetric 7a areas of the two hemispheres.Time lags for maximum interactions were longer for opposite- vs. same-hemisphere recordings, and lags for negative interactions were longer than for positive interactions in both recording sites.These findings underscore the involvement of dynamic neuronal interactions in cognitive processing within and across hemispheres.

View Article: PubMed Central - PubMed

Affiliation: Department of Behavioral and Cognitive Neurobiology, Instituto de Neurobiología, UNAM Querétaro, México.

ABSTRACT
Traditionally, the neurophysiological mechanisms of cognitive processing have been investigated at the single cell level. Here we show that the dynamic, millisecond-by-millisecond, interactions between neuronal events measured by local field potentials are modulated in an orderly fashion by key task variables of a space categorization task performed by monkeys. These interactions were stronger during periods of higher cognitive load and varied in sign (positive, negative). They were observed both within area 7a of the posterior parietal cortex and between symmetric 7a areas of the two hemispheres. Time lags for maximum interactions were longer for opposite- vs. same-hemisphere recordings, and lags for negative interactions were longer than for positive interactions in both recording sites. These findings underscore the involvement of dynamic neuronal interactions in cognitive processing within and across hemispheres. They also provide accurate estimates of lags in callosal interactions, very comparable to similar estimates of callosal conduction delays derived from neuroanatomical measurements (Caminiti et al., 2013).

No MeSH data available.


Related in: MedlinePlus